Capitalizing on machine learning—from life sciences to financial services

December 26, 2016

The promise of machine learning has a science fiction flavor to it: computer programs that learn from their experiences and get better and better at what they do. So is machine learning fact or fiction?

The global marketplace answers this question emphatically: Machine learning is not just real; it is a booming field of technology that is being applied in countless artificial intelligence (AI) applications, ranging from crop monitoring and drug development to fraud detection and autonomous vehicles. Collectively, the global AI market is expected to be worth more than $16 billion by 2022, according to the research firm MarketsandMarkets.[1]

In the life sciences arena, researchers are leveraging machine learning in their work to drive groundbreaking discoveries that may help improve the health and wellbeing of people. This research is taking place around the world.

In the United States, for example, researchers at the MIT Lincoln Laboratory Supercomputing Center (LLSC) are applying the power of machine learning algorithms and a new Dell EMC Top500 supercomputer to ferret out patterns in massive amounts of patient data collected from publicly available sources. These scientific investigations could potentially lead to faster personalized treatments and the discovery of cures.

In one such project, researchers affiliated with the LLSC used the new Top500 supercomputer to gain insights from an enormous amount of data collected from an intensive care unit over 10 years. “We did analytics and analysis on this data that was not possible before,” says LLSC researcher Vijay Gadepally. “We were able to reduce two to 10 times the amount of time taken to do analysis, such as finding patients who have similar waveforms.” Watch the video.

In China, Dell EMC is collaborating with the Chinese Academy of Sciences on a joint artificial intelligence and advanced computing laboratory. This lab focuses on research and applications of new computing architectures in the fields of brain information processing and artificial intelligence. Research conducted in the lab spans cognitive function simulation, deep learning, brain computer simulation, and related new computing systems. The lab also supports the development of brain science and intellect technology research, promoting Chinese innovation and breakthroughs at the forefront of science. In fact, Dell China was recently honored with an “Innovation Award of Artificial Intelligence in Technology & Practice” award in recognition of the collaboration. Read the blog.

In Europe, meanwhile, the University of Pisa is using deep learning technologies and systems from Dell EMC for DNA sequencing, encoding DNA as an image. The examples like these could go on and on, because the application of machine learning techniques in the life sciences has tremendous momentum in laboratories around the world.

So why does this matter? In short, because we need to gain insights from massive amounts of data, and this process requires systems that exceed human capabilities. Machine learning algorithms can dig through mountains of data to ferret patterns that might not otherwise be recognizable. Moreover, machine learning algorithms get better over time, because they learn from their experiences.

In the healthcare arena, machine learning promises to drive life-saving advances in patient care. “While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care,” notes author Bernard Marr, writing in Forbes. “Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care.”[2]

Machine learning is also making wide inroads in diverse industries and commercial applications. MasterCard, for example, is using machine learning to detect fraud, while Facebook is putting machine learning technologies to work via a facial recognition algorithm that continually improves its performance. Watch the video.

“Machine learning has become extremely popular,” says Jeremy Kepner, Laboratory Fellow and head of the MIT Lincoln Laboratory Supercomputing Center. “Computers can see now. That’s something that I could not say five years ago. That technology is now being applied everywhere. It’s so much easier when you can point a camera at something and it can then produce an output of all the things that were in that image.” Watch the video.

Here’s the bottom line: Machine learning is no longer the stuff of science fiction. It’s very real, it’s here today and it’s getting better all the time—in life sciences and fields beyond.

Ready to get started with machine learning?

Here are some ways to further your understanding of what machine learning systems could do for your organization:

  • Many courses available in machine learning.
  • A growing number of open source communities are driving advances in machine learning. You can find links to communities and other resources in the Intel Developer Zone.
  • Dell EMC | Intel HPC Innovation Centers around the world offer opportunities for technical collaboration and early access to technology.

 

[1] MarketsandMarkets. “Artificial Intelligence Market by Technology (Deep Learning, Robotics, Digital Personal Assistant, Querying Method, Natural Language Processing, Context Aware Processing), Offering, End-User Industry, and Geography – Global Forecast to 2022.” November 2016.

[2] Bernard Marr. “How Machine Learning, Big Data And AI Are Changing Healthcare Forever.” Forbes. Sept. 23, 2016

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